Measuring creative employment: Implications for innovation policy
Stuart Cunningham
ARC Centre of Excellence for Creative Industries and Innovation, Queensland University of Technology, Brisbane QLD
Peter Higgs
ARC Centre of Excellence for Creative Industries and Innovation, Queensland University of Technology, Brisbane QLD
PP: 190 - 200
Abstract
Both creative industries and innovation are slippery fish to handle conceptually, to say nothing of their relationship.
This paper faces, first, the problems of definitions and data that can bedevil clear analysis of the creative industries. It then presents a method of data generation and analysis that has been developed to address these problems while providing an evidence pathway supporting the movement in policy thinking from creative output (through industry sectors) to creative input to the broader economy (through a focus on occupations/activity).
Facing the test of policy relevance, this work has assisted in moving the ongoing debates about the creative industries toward innovation thinking by developing the concept of creative occupations as input value. Creative inputs as 'enablers' arguably has parallels with the way ICTs have been shown to be broad enablers of economic growth. We conclude with two short instantiations of the policy relevance of this concept: design as a creative input; and creative human capital and education.
Keywords
creative industries, trident, innovation policy
Article Text
There is no question that definitional wrangling over what counts as creative industries has limited its uptake. There is almost exasperation in Simon Roodhouse's survey of what he calls the 'tortuous and contorted definitional history' of the arts, cultural and creative industries (Roodhouse 2001: 505). There are contending analytical and statistical categories such as copyright industries, content industries, cultural industries, digital content, the arts or entertainment industries, and more. This category confusion means that it is difficult to gather accurate, authoritative and timely data about sectors and that it is subject to unfocused analysis and intervention.
A survey of the data challenges faced by the creative industries notes the 'extremely difficult statistical measurement issues to overcome' (Pattinson Consulting 2003: 6). These issues are part of the broader challenges of measuring effectively domains undergoing substantial change through the progressive convergence of the computer, communication, cultural and content industries. This is the subject of a growing academic and policy literature (eg Burns Owens Partnership et al. 2006; Pattinson 2003; Pratt 2000, 2004, 2008; Wyszomirski 2008). New hybrid occupations and industry sectors emerge that do not comfortably fit into standard statistics classifications. The 10-15 year gap between updates of these classification schemes means there is almost no comprehensive, standardized employment or industry data available during the critical emergence period of many sectors. Measuring the production and purchasing of physical products is difficult enough but measuring the number, size and value of the delivery of services is an order of magnitude more difficult. The challenges in seeking to measure the flow-on impact of emergent digital creative industries services to other sectors of the economy are even greater.
Having readily conceded the degree of difficulty - one faced by all jurisdictions, supra-, inter- and sub-national as well as national - it must also be said that progress is being made on better data that is statistically robust and of value in the development of policy (see Higgs & Cunningham 2008). Productive effort has been made at the intergovernmental level at organizations such as the United Nations Educational, Science & Cultural Organization (UNESCO), the United Nations Conference on Trade and Development (UNCTAD), the Organisation for Economic Co-operation and Development (OECD), the World Intellectual Property Organization (WIPO) and the United Nations Development Programme (UNDP). At the national level, there have been substantial mapping exercises in the U.K., Hong Kong, Singapore, Australia, New Zealand, France and in other locations at the sub-national and local levels. Specific sectors of the creative industries have been the focus of concerted work to map their size and impact on the wider economy (for example, design in Ontario, Victoria, New Zealand and the U.K.). And at the cutting edge of policy-relevant data analytics, there is progress being made on defining the 'creative economy', which can be taken to mean the contribution which the creative workforce and/or the creative industries sectors themselves are making to their national or regional economies (Bakhshi, McVittie & Simmie 2008; Higgs, Cunningham & Bakhshi 2008; Higgs, Cunningham & Pagan 2007a, b).
The data challenges faced by policy makers and analysts seeking to grasp the size, growth rates, economic impact and links with the wider economy of the creative industries are an integral part of the productive ferment evidenced as economies and societies undergo rapid change due to digitization, convergence, the growth of knowledge-intensive services and services-based economies more generally. The very difficulties are themselves an indicator of significance.
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